Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29392
Title: Regularized discriminant entropy analysis
Authors: Zhao, H
Wong, WK 
Keywords: Discriminant entropy analysis
Entropy-based learning
Regularized discriminant entropy
Issue Date: 2014
Publisher: Elsevier Sci Ltd
Source: Pattern recognition, 2014, v. 47, no. 2, p. 806-819 How to cite?
Journal: Pattern Recognition 
Abstract: In this paper, we propose the regularized discriminant entropy (RDE) which considers both class information and scatter information on original data. Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA). RDEA is quite simple and requires no approximation in theoretical derivation. The experiments with several publicly available data sets show the feasibility and effectiveness of the proposed algorithm with encouraging results.
URI: http://hdl.handle.net/10397/29392
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2013.08.020
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